What type of modeling is used to predict the dispersion of airborne pollutants?

Gaussian dispersion modeling is a widely used method for predicting the dispersion of airborne pollutants. This type of modeling is based on the Gaussian distribution, which describes how pollutants disperse in the atmosphere under certain conditions. It considers factors such as wind speed, wind direction, atmospheric stability, and the height of the emission source.

In practice, Gaussian models are effective for estimating the concentration of pollutants at different distances from the emission source over time. They assume that the pollutants disperse in a normal, bell-shaped curve, leading to predictable patterns of concentration based on the specified environmental conditions. This approach is particularly useful for regulatory modeling, air quality assessments, and emergency response planning because it provides a straightforward way to visualize how pollutants will spread through the atmosphere.

Other options, such as Geographic Information Systems (GIS), are important tools for mapping and analyzing spatial data but do not focus specifically on the physics of dispersion like Gaussian models do. Statistical Process Control (SPC) relates more to monitoring and controlling processes rather than predicting pollutant dispersion. Financial forecasting is unrelated to environmental modeling, as it deals with predicting future financial trends based on historical data. Thus, Gaussian dispersion modeling stands out as the correct answer for predicting airborne pollutant dispersion.